Primary exploratory analysis

Filter cells according to their quality metrics

Parameter Min threshold Max threshold
nFeature_RNA 400 +Inf
nCount_RNA -Inf 20000
Percent.mito -Inf 0.1
Dropouts -Inf 0.97

Before 1051 cells –> After 1026 cells


Identification of highly variable genes (HVGs) and dimension reduction (PCA)

Median UMI per cell : 1587.5

Nb highly variable genes : 2000

## PC_ 1 
## Positive:  VIM, SPARCL1, AQP1, CAV1, ID3, GNG11, ID1, DARC, TSPAN7, IGFBP4 
##     VWF, PLVAP, CLEC14A, KRT15, RAMP2, ECSCR, EMCN, CD34, ENG, A2M 
##     CLDN5, ESAM, TMEM88, COX7A1, HSPG2, CCL14, ELTD1, PRCP, NRN1, TIMP3 
## Negative:  C1orf194, C9orf24, ATPIF1, FAM183A, C9orf116, RSPH1, AC013264.2, SNTN, DYNLL1, C5orf49 
##     CETN2, C20orf85, PIFO, CAPS, SMIM22, MORN2, AGR3, TPPP3, DYNLRB2, B9D1 
##     ODF3B, AKAP14, TUBB4B, MORN5, FAM229B, C12orf75, NME5, CRIP1, LRRC46, RP11-128M1.1 
## PC_ 2 
## Positive:  SPARCL1, IGFBP7, VIM, AQP1, GNG11, SNTN, CAV1, ID3, DARC, VWF 
##     C1orf194, AC013264.2, RSPH1, TNFAIP8L1, TSPAN7, C20orf85, STOML3, FAM92B, MORN5, RP11-128M1.1 
##     ODF3B, TUBA1A, PPP1R42, C2orf40, CCDC146, PROS1, PLVAP, CD34, ENG, C9orf24 
## Negative:  MGST1, CXCL1, MDK, CXCL17, VMO1, WFDC2, KRT19, FAM3D, CTSC, ELF3 
##     TSPAN8, SCGB1A1, AGR2, KRT7, SLPI, RHOV, CYP2F1, TACSTD2, CD55, XBP1 
##     IL8, CTD-2531D15.4, CP, CLDN7, LCN2, KRT18, CXCL6, CLCA2, EPCAM, KLK11 
## PC_ 3 
## Positive:  KRT15, S100A2, SOD3, TAGLN, ACTA2, TPM2, MYL9, TPM1, DSTN, MT1X 
##     KRT19, PDLIM3, CNN1, PPP1R14A, ACTG2, DES, TACSTD2, MYH11, RP5-977B1.11, MYLK 
##     RARRES2, MFAP4, AEBP1, PLN, CKB, CSRP1, BCHE, FXYD1, PCSK7, ASPN 
## Negative:  CD74, TMSB4X, HLA-DRA, FCER1G, HLA-DPB1, AIF1, TYROBP, HLA-DPA1, HLA-DRB1, HLA-DMA 
##     HLA-DQB1, HLA-DQA2, CST3, ITGB2, MS4A6A, TMSB10, HLA-DRB5, MNDA, HLA-DQA1, CORO1A 
##     CPVL, TBXAS1, ARHGDIB, HLA-DQB2, HLA-DMB, HCST, COTL1, TYMP, AOAH, LY86 
## PC_ 4 
## Positive:  TAGLN, ACTA2, MYL9, TPM2, PPP1R14A, SOD3, ACTB, CNN1, ACTG2, TPM1 
##     PDLIM3, LGALS1, DSTN, MYLK, DES, PCSK7, MYH11, AEBP1, BCHE, MFAP4 
##     RP5-977B1.11, PLN, CSRP1, FHL1, FLNA, ASPN, PDLIM7, COX7A1, FAM83D, CALD1 
## Negative:  ID1, GNG11, DARC, AQP1, TSPAN7, IFI27, ID3, VWF, PLVAP, RAMP2 
##     KRT15, EMCN, ECSCR, EGFL7, CCL14, NRN1, CLDN5, ENG, S100A2, ELTD1 
##     TMEM88, KLF2, CLEC14A, JAM2, ESAM, PRCP, ITM2A, TM4SF1, NOSTRIN, RAMP3 
## PC_ 5 
## Positive:  KRT15, S100A2, FCER1G, AIF1, TYROBP, MT1X, RNASET2, ITGB2, MS4A6A, HCST 
##     MNDA, CORO1A, LAPTM5, PTPRC, TBXAS1, PRDX5, CSF1R, LST1, LY86, ALDH3A1 
##     ALOX5AP, AOAH, CD68, LGALS2, RNASE6, EMP3, FGL2, KRT19, CTSS, EVI2B 
## Negative:  SPARCL1, VIM, IGFBP7, GNG11, AQP1, ID3, ID1, DARC, IFI27, TSPAN7 
##     PLVAP, VWF, ECSCR, EMCN, ENG, EGFL7, RAMP2, CLDN5, CCL14, ELTD1 
##     CD34, PRCP, NRN1, CAV1, KLF2, HYAL2, ESAM, JAM2, CLEC14A, TM4SF1

Nb PCs to use : PCs : 1:10

Dataset embbedings (t-SNE and UMAP) and clustering (Louvain algorithm)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 1026
## Number of edges: 13572
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9102
## Number of communities: 11
## Elapsed time: 0 seconds

Distribution of the quality metrics per cluster

Identification of cluster marker genes (Wilcoxon’s rank test)



Cell type labelling
## 
##         Basal    Suprabasal   Endothelial Smooth muscle    Macrophage 
##           364           180           233            89            50 
##     Secretory    Fibroblast        Serous Multiciliated 
##            33            32            25            20

Recompute top marker genes for each cell population

## R version 3.5.2 (2018-12-20)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 10 (buster)
## 
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.3.5.so
## 
## locale:
##  [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8    
##  [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
##  [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ggrepel_0.8.0     cowplot_0.9.4     ggplot2_3.1.0     dplyr_0.8.3      
## [5] tidyr_0.8.2       Seurat_3.0.0.9000
## 
## loaded via a namespace (and not attached):
##  [1] httr_1.4.0          viridisLite_0.3.0   jsonlite_1.6       
##  [4] splines_3.5.2       lsei_1.2-0          R.utils_2.8.0      
##  [7] gtools_3.8.1        Rdpack_0.10-1       assertthat_0.2.1   
## [10] yaml_2.2.0          globals_0.12.4      pillar_1.3.1       
## [13] lattice_0.20-38     reticulate_1.11.1   glue_1.3.0         
## [16] digest_0.6.20       RColorBrewer_1.1-2  SDMTools_1.1-221   
## [19] colorspace_1.4-0    htmltools_0.3.6     Matrix_1.2-16      
## [22] R.oo_1.22.0         plyr_1.8.4          pkgconfig_2.0.2    
## [25] bibtex_0.4.2        tsne_0.1-3          listenv_0.7.0      
## [28] purrr_0.3.3         scales_1.0.0        RANN_2.6.1         
## [31] gdata_2.18.0        Rtsne_0.15          tibble_2.0.1       
## [34] withr_2.1.2         ROCR_1.0-7          pbapply_1.4-0      
## [37] lazyeval_0.2.1      survival_2.43-3     magrittr_1.5       
## [40] crayon_1.3.4        evaluate_0.13       R.methodsS3_1.7.1  
## [43] future_1.12.0       nlme_3.1-137        MASS_7.3-51.1      
## [46] gplots_3.0.1.1      ica_1.0-2           tools_3.5.2        
## [49] fitdistrplus_1.0-14 data.table_1.12.2   gbRd_0.4-11        
## [52] stringr_1.4.0       plotly_4.8.0        munsell_0.5.0      
## [55] cluster_2.0.7-1     irlba_2.3.3         compiler_3.5.2     
## [58] rsvd_1.0.0          caTools_1.17.1.1    rlang_0.4.0        
## [61] grid_3.5.2          ggridges_0.5.1      htmlwidgets_1.3    
## [64] igraph_1.2.4        labeling_0.3        bitops_1.0-6       
## [67] rmarkdown_1.11      npsurv_0.4-0        gtable_0.2.0       
## [70] codetools_0.2-16    R6_2.4.0            zoo_1.8-5          
## [73] knitr_1.21          future.apply_1.2.0  KernSmooth_2.23-15 
## [76] metap_1.1           ape_5.3             stringi_1.2.4      
## [79] parallel_3.5.2      Rcpp_1.0.3          png_0.1-7          
## [82] tidyselect_0.2.5    xfun_0.4            lmtest_0.9-36